Doug Hellmann

Suggesting PyMOTW topics via Skribit

2009-07-13T08:53:00.001-04:00

I've added a Skribit widget to the PyMOTW home page and to my blog to get some feedback from readers about what modules or general topics I should cover next. Add your suggestion to the list, or vote for one of the existing suggestions, to let me know what you want to read about. I'll monitor the suggestions and use them to decide which module(s) to start researching for my next article.

No account is necessary to participate, so let me hear from you today!

PyMOTW: File Access

2009-07-12T08:30:00.001-04:00

File Access

Python’s standard library includes a large range of tools for working with files, filenames, and file contents.

Filenames

The first step in working with files is to get the name of the file so you can operate on it. Python represents filenames as simple strings, but provides tools for building them from standard, platform-independent, components in os.path. List the contents of a directory with listdir() from os, or use glob to build a list of filenames from a pattern. Finer grained filtering of filenames is possible with fnmatch.

Meta-data

Once you know the name of the file, you may want to check other characteristics such as permissions or the file size using os.stat() and the constants in stat.

Reading Files

If you’re writing a filter application that processes text input line-by-line, fileinput provides an easy framework to get started. The fileinput API calls for you to iterate over the input() generator, processing each line as it is yielded. The generator handles parsing command line arguments for file names, or falling back to reading directly from sys.stdin. The result is a flexible tool your users can run directly on a file or as part of a pipeline.

If your app needs random access to files, linecache makes it easy to read lines by their line number. The contents of the file are maintained in a cache, so be careful of memory consumption.

Temporary Files

For cases where you need to create scratch files to hold data temporarily, or before moving it to a permanent location, tempfile will be very useful. It provides classes to create temporary files and directories safely and securely. Names are guaranteed not to collide, and include random components so they are not easily guessable.

Files and Directories

Frequently you need to work on a file as a whole, without worrying about what is in it. The shutil module includes high-level file operations such as copying files and directories, setting permissions, etc.

PyMOTW Home

The canonical version of this article

New command line interface to PyMOTW

2009-07-05T09:30:00.001-04:00

The 1.95 release of PyMOTW includes a command line interface to access the documentation for a module.

The package can be installed via easy_install or pip:

$ pip install PyMOTW
Downloading/unpacking PyMOTW
  Downloading PyMOTW-1.95.tar.gz (2.2Mb): 2.2Mb downloaded
  Running setup.py egg_info for package PyMOTW
    warning: no files found matching 'ChangeLog'
    warning: no files found matching '*.py' under directory 'sphinx/templates'
    no previously-included directories found matching 'utils'
Installing collected packages: PyMOTW
  Running setup.py install for PyMOTW
    changing mode of build/scripts-2.6/motw from 644 to 755
    warning: no files found matching 'ChangeLog'
    warning: no files found matching '*.py' under directory 'sphinx/templates'
    no previously-included directories found matching 'utils'
    changing mode of /Users/dhellmann/.virtualenvs/testpymotw/bin/motw to 755
Successfully installed PyMOTW

and then to use the command line interface, run motw.

$ motw -h
Usage: motw [options] 

Options:
  -h, --help  show this help message and exit
  -t, --text  Print plain-text version of help to stdout
  -w, --web   Open HTML version of help from web
  --html      Open HTML version of help from installed file

For example, motw abc opens the local version of this week's article. You can also use the "-w" option to go to my web site instead of reading the local version, so you always have the latest version of an article.

PyMOTW: abc - Abstract Base Classes

2009-07-05T09:18:00.001-04:00

abc – Abstract Base Classes

Purpose:	Define and use abstract base classes for API checks in your code.
Python Version:	2.6

Why use Abstract Base Classes?

Abstract base classes are a form of interface checking more strict than individual hasattr() checks for particular methods. By defining an abstract base class, you can define a common API for a set of subclasses. This capability is especially useful in situations where a third-party is going to provide implementations, such as with plugins to an application, but can also aid you when working on a large team or with a large code-base where keeping all classes in your head at the same time is difficult or not possible.

How ABCs Work

abc works by marking methods of the base class as abstract, and then registering concrete classes as implementations of the abstract base. If your code requires a particular API, you can use issubclass() or isinstance() to check an object against the abstract class.

Let’s start by defining an abstract base class to represent the API of a set of plugins for saving and loading data.

import abc

class PluginBase(object):
    __metaclass__ = abc.ABCMeta
    
    @abc.abstractmethod
    def load(self, input):
        """Retrieve data from the input source and return an object."""
        return
    
    @abc.abstractmethod
    def save(self, output, data):
        """Save the data object to the output."""
        return

Registering a Concrete Class

There are two ways to indicate that a concrete class implements an abstract: register the class with the abc or subclass directly from the abc.

import abc
from abc_base import PluginBase

class RegisteredImplementation(object):
    
    def load(self, input):
        return input.read()
    
    def save(self, output, data):
        return output.write(data)

PluginBase.register(RegisteredImplementation)

if __name__ == '__main__':
    print 'Subclass:', issubclass(RegisteredImplementation, PluginBase)
    print 'Instance:', isinstance(RegisteredImplementation(), PluginBase)

In this example the PluginImplementation is not derived from PluginBase, but is registered as implementing the PluginBase API.

$ python abc_register.py
Subclass: True
Instance: True

Implementation Through Subclassing

By subclassing directly from the base, we can avoid the need to register the class explicitly.

import abc
from abc_base import PluginBase

class SubclassImplementation(PluginBase):
    
    def load(self, input):
        return input.read()
    
    def save(self, output, data):
        return output.write(data)

if __name__ == '__main__':
    print 'Subclass:', issubclass(SubclassImplementation, PluginBase)
    print 'Instance:', isinstance(SubclassImplementation(), PluginBase)

In this case the normal Python class management is used to recognize PluginImplementation as implementing the abstract PluginBase.

$ python abc_subclass.py
Subclass: True
Instance: True

A side-effect of using direct subclassing is it is possible to find all of the implementations of your plugin by asking the base class for the list of known classes derived from it (this is not an abc feature, all classes can do this).

import abc
from abc_base import PluginBase
import abc_subclass
import abc_register

for sc in PluginBase.__subclasses__():
    print sc.__name__

Notice that even though abc_register is imported, RegisteredImplementation is not among the list of subclasses because it is not actually derived from the base.

$ python abc_find_subclasses.py
SubclassImplementation

Dr. Andr√© Roberge has described using this capability to discover plugins by importing all of the modules in a directory dynamically and then looking at the subclass list to find the implementation classes.

Incomplete Implementations

Another benefit of subclassing directly from your abstract base class is that the subclass cannot be instantiated unless it fully implements the abstract portion of the API. This can keep half-baked implementations from triggering unexpected errors at runtime.

import abc
from abc_base import PluginBase

class IncompleteImplementation(PluginBase):
    
    def save(self, output, data):
        return output.write(data)

PluginBase.register(IncompleteImplementation)

if __name__ == '__main__':
    print 'Subclass:', issubclass(IncompleteImplementation, PluginBase)
    print 'Instance:', isinstance(IncompleteImplementation(), PluginBase)

$ python abc_incomplete.py
Subclass: True
Instance:
Traceback (most recent call last):
  File "abc_incomplete.py", line 22, in <module>
    print 'Instance:', isinstance(IncompleteImplementation(), PluginBase)
TypeError: Can't instantiate abstract class IncompleteImplementation with abstract methods load

Concrete Methods in ABCs

Although a concrete class must provide an implementation of an abstract methods, the abstract base class can also provide an implementation that can be invoked via super(). This lets you re-use common logic by placing it in the base class, but force subclasses to provide an overriding method with (potentially) custom logic.

import abc
from cStringIO import StringIO

class ABCWithConcreteImplementation(object):
    __metaclass__ = abc.ABCMeta
    
    @abc.abstractmethod
    def retrieve_values(self, input):
        print 'base class reading data'
        return input.read()

class ConcreteOverride(ABCWithConcreteImplementation):
    
    def retrieve_values(self, input):
        base_data = super(ConcreteOverride, self).retrieve_values(input)
        print 'subclass sorting data'
        response = sorted(base_data.splitlines())
        return response

input = StringIO("""line one
line two
line three
""")

reader = ConcreteOverride()
print reader.retrieve_values(input)
print

Since ABCWithConcreteImplementation is an abstract base class, it isn’t possible to instantiate it to use it directly. Subclasses must provide an override for retrieve_values(), and in this case the concrete class massages the data before returning it at all.

$ python abc_concrete_method.py
base class reading data
subclass sorting data
['line one', 'line three', 'line two']

Abstract Properties

If your API specification includes attributes in addition to methods, you can require the attributes in concrete classes by defining them with @abstractproperty.

import abc

class Base(object):
    __metaclass__ = abc.ABCMeta
    
    @abc.abstractproperty
    def value(self):
        return 'Should never get here'


class Implementation(Base):
    
    @property
    def value(self):
        return 'concrete property'


try:
    b = Base()
    print 'Base.value:', b.value
except Exception, err:
    print 'ERROR:', str(err)

i = Implementation()
print 'Implementation.value:', i.value

The Base class in the example cannot be instantiated because it has only an abstract version of the property getter method.

$ python abc_abstractproperty.py
ERROR: Can't instantiate abstract class Base with abstract methods value
Implementation.value: concrete property

You can also define abstract read/write properties.

import abc

class Base(object):
    __metaclass__ = abc.ABCMeta
    
    def value_getter(self):
        return 'Should never see this'
    
    def value_setter(self, newvalue):
        return

    value = abc.abstractproperty(value_getter, value_setter)


class PartialImplementation(Base):
    
    @abc.abstractproperty
    def value(self):
        return 'Read-only'


class Implementation(Base):
    
    _value = 'Default value'
    
    def value_getter(self):
        return self._value

    def value_setter(self, newvalue):
        self._value = newvalue

    value = property(value_getter, value_setter)


try:
    b = Base()
    print 'Base.value:', b.value
except Exception, err:
    print 'ERROR:', str(err)

try:
    p = PartialImplementation()
    print 'PartialImplementation.value:', p.value
except Exception, err:
    print 'ERROR:', str(err)

i = Implementation()
print 'Implementation.value:', i.value

i.value = 'New value'
print 'Changed value:', i.value

Notice that the concrete property must be defined the same way as the abstract property. Trying to override a read/write property in PartialImplementation with one that is read-only does not work.

$ python abc_abstractproperty_rw.py
ERROR: Can't instantiate abstract class Base with abstract methods value
ERROR: Can't instantiate abstract class PartialImplementation with abstract methods value
Implementation.value: Default value
Changed value: New value

Unfortunately, the decorator syntax does not work for read/write abstract properties the way it does with concrete properties.

import abc

class Base(object):
    __metaclass__ = abc.ABCMeta
    
    @abc.abstractproperty
    def value(self):
        return 'Should never see this'
    
    @value.setter
    def value_setter(self, newvalue):
        return


class Implementation(Base):
    
    _value = 'Default value'
    
    @property
    def value(self):
        return self._value

    @value.setter
    def value_setter(self, newvalue):
        self._value = newvalue


i = Implementation()
print 'Implementation.value:', i.value

i.value = 'New value'
print 'Changed value:', i.value

Notice that the caller cannot set the property value.

$ python abc_abstractproperty_rw_deco.py
Implementation.value: Default value
Traceback (most recent call last):
  File "abc_abstractproperty_rw_deco.py", line 40, in <module>
    i.value = 'New value'
AttributeError: can't set attribute

Collection Types

The collections module defines several abstract base classes related to container (and containable) types.

General container classes:

Container
Sized

Iterator and Sequence classes:

Iterable
Iterator
Sequence
MutableSequence

Unique values:

Hashable
Set
MutableSet

Mappings:

Mapping
MutableMapping
MappingView
KeysView
ItemsView
ValuesView

Miscelaneous:

Callable

In addition to serving as detailed real-world examples of abstract base classes, Python’s built-in types are automatically registered to these classes when you import collections. This means you can safely use isinstance() to check parameters in your code to ensure that they support the API you need. The base classes can also be used to define your own collection types, since many of them provide concrete implementations of the internals and only need a few methods overridden. Refer to the standard library docs for collections for more details.